In many recent optical measurement systems, if a measured object's surface is not evenly formed with respect to its texture, structures or even roughness, most often that the intensities of reflected/scattered lights from different portions of the object's surface can be varying in a very wide range. In order to produce an image of the object's surface with wide range of brightness variations, a single-point photo detector with high dynamic range is required. However, when the aforesaid optical measurement system with single-point photo detector is used for performing a larger-area measurement, a precision transportation mechanism is required for controlling the single-point photo detector to move to all points of the measured area accurately. Not to mention the disadvantage that the larger the area to be measured, the longer the time it will require to complete the measurement. On the other hand, When a larger-area photo detector, such as CCD, or CMOS, is used for imaging, it is usually that any image captured by such conventional imaging device ends up being too dark in some areas and saturated in others since their dynamic range is not sufficient, and thus measurement error resulting from the images captured thereby can be caused. It is noted that there are already many method designed for overcoming the aforesaid problem of insufficient dynamic range. On of which is to sequentially capture multiple images of the same scene using different exposures, and thus the multiple images are combined into a single high dynamic range image according to a specific algorithm of complementary. Another method is by placing an optical mask adjacent to the conventional imaging devices for giving the adjacent pixels on the imaging devices different exposures to the scene, and then the method performs an image reconstruction process of interpolation so as to achieve high dynamic range image.
Moreover, there is another related research disclosed in “High Dynamic Range Imaging Spatially Varying Pixel Exposures”, Computer Vision and Pattern Recognition, Proceedings. IEEE Conference on Volume 1, 13-15 Jun. 2000, by Shree K. Nayar and Tomoo Mitsunaga. Please refer to FIG. 1A and FIG. 1B, which are schematic diagrams showing a conventional imaging system with spatially varying pixel exposures and an optical mask used in the imaging system. As shown in FIG. 1A and FIG. 1B, the aforesaid disclosure is featured by introducing an optical mask with a pattern of spatially varying transmittances 92 into the imaging system at a position between its imaging device 90, e.g. a CCD, and an object to be measured 91. Considering the array of pixels shown in FIG. 1B, the brightness level associated with each pixel represents its sensitivity, such that, the brighter pixels have greater exposure to image irradiance and the darker ones have lower exposure. Noted that when a pixel is saturated in the acquired image, it is likely to have a neighbor that is not. Thereby, a high dynamic range image can be computed according to a specific algorithm for combining four neighboring pixels of different exposures into one larger single pixel including the four neighboring pixels, however, at a cost of lower spatial resolution.